Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for workforce management in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to perform workforce management operations in the contact center system, historical contact-agent interaction data of the contact center system; determining, by the at least one computer processor, a preferred amount of choice for a choice-based pairing strategy for selecting waiting contacts out of queue order based on the historical contact-agent interaction data; determining, by the at least one computer processor, an adjustment to an agent workforce of the contact center system to target the preferred amount of choice, wherein the adjustment comprises a decrease to the agent workforce; applying, by the at least one computer processor, the choice-based pairing strategy to select waiting contacts out of queue order, wherein the targeting of the preferred amount of choice improves performance of the contact center system; and establishing, in a switch of the contact center system, a communications channel between a waiting contact and an available agent based upon the applying of the choice-based pairing strategy to realize a performance gain for the contact center system attributable to the choice-based pairing strategy and the targeting of the preferred amount of choice.
Workforce management in contact centers. This invention addresses the problem of optimizing agent workforce size and contact selection to improve contact center performance. The method involves analyzing past interactions between contacts and agents to understand historical data. Based on this historical data, a desired level of flexibility, referred to as a "preferred amount of choice," is determined for a strategy that selects waiting contacts out of their standard queue order. This strategy allows for non-sequential contact assignment. An adjustment is then made to the number of agents available in the workforce. This adjustment specifically aims to achieve the determined preferred amount of choice and involves reducing the agent workforce. The choice-based pairing strategy is then implemented to select waiting contacts out of queue order. By targeting the preferred amount of choice, the overall performance of the contact center is enhanced. Finally, a communication channel is established between a waiting contact and an available agent using this choice-based pairing strategy. This process leads to a performance improvement in the contact center, attributed to both the non-sequential contact selection and the targeted reduction in workforce size.
2. The method of claim 1 , wherein determining the adjustment further comprises: determining, by the at least one computer processor, historical workforce size data or historical workforce capacity data.
This invention relates to workforce management systems that optimize staffing levels based on historical data. The problem addressed is the inefficiency in workforce planning due to reliance on static or incomplete data, leading to either understaffing or overstaffing. The solution involves dynamically adjusting workforce size or capacity by analyzing historical workforce size data or historical workforce capacity data. This historical data helps predict future staffing needs more accurately, ensuring optimal resource allocation. The system uses at least one computer processor to process this data, enabling real-time adjustments to workforce levels. By incorporating historical trends, the method improves decision-making for staffing, reducing costs and enhancing operational efficiency. The invention is particularly useful in industries with fluctuating demand, such as healthcare, retail, or customer service, where precise workforce planning is critical. The historical data may include past staffing levels, productivity metrics, or capacity constraints, allowing the system to identify patterns and make data-driven adjustments. This approach ensures that workforce size or capacity aligns with actual demand, minimizing waste and improving service quality. The method may also integrate with other workforce management tools to provide a comprehensive solution for staffing optimization.
3. The method of claim 1 , wherein the adjustment further comprises an increase to the agent workforce.
A system and method for dynamically adjusting an agent workforce in a customer service or support environment addresses the challenge of efficiently managing fluctuating workloads to maintain service quality. The method involves monitoring real-time performance metrics such as response times, call volumes, and agent availability to identify bottlenecks or inefficiencies. When performance metrics fall below predefined thresholds, the system automatically triggers adjustments to the agent workforce. These adjustments include increasing the number of agents to handle higher call volumes or redistributing workloads among available agents to balance the load. The system may also integrate with scheduling tools to bring additional agents online or reassign tasks based on priority and skill sets. By dynamically scaling the workforce, the system ensures optimal resource utilization while minimizing wait times and improving customer satisfaction. The method may further incorporate predictive analytics to anticipate demand spikes and proactively adjust staffing levels before performance degradation occurs. This approach reduces operational costs by avoiding overstaffing during low-demand periods while ensuring adequate coverage during peak times. The system can be applied in call centers, chat support, or other service environments where workforce flexibility is critical.
4. The method of claim 1 , wherein the decrease to the agent workforce is initiated, and wherein an increase in contact queue size is expected based on the decrease to the agent workforce.
This invention relates to workforce management in contact centers, specifically addressing the challenge of dynamically adjusting agent staffing levels while anticipating changes in contact queue sizes. The method involves reducing the number of agents in a workforce and predicting an increase in the contact queue size as a result of this reduction. The system monitors contact center operations, detects conditions that warrant a decrease in agent workforce, and initiates the reduction while simultaneously forecasting the impact on queue size. This ensures that the contact center can proactively manage workload distribution, prevent service degradation, and maintain operational efficiency despite staffing changes. The method may include analyzing historical data, real-time metrics, and predictive algorithms to determine the optimal timing and extent of workforce adjustments. By anticipating queue size increases, the system allows for preemptive measures such as rerouting contacts, adjusting service levels, or deploying additional resources to mitigate potential bottlenecks. The invention is particularly useful in environments where agent availability fluctuates, such as during peak hours, staffing shortages, or unexpected disruptions, ensuring smooth contact center operations.
5. The method of claim 1 , wherein the decrease to the agent workforce is initiated, and wherein an increase in a duration of an L2 state is expected.
This invention relates to workforce management in a technical support system, specifically addressing the challenge of optimizing agent resources while maintaining service quality during periods of reduced staffing. The method involves dynamically adjusting the workforce based on predicted changes in service demand, particularly when a decrease in the agent workforce is anticipated. The system monitors key performance indicators (KPIs) such as call volume, resolution times, and customer satisfaction to assess the impact of workforce changes. When a reduction in agents is initiated, the system also expects an increase in the duration of an L2 (Level 2) support state, indicating more complex issues requiring deeper troubleshooting. To mitigate potential service degradation, the method may involve redistributing workloads, extending agent shifts, or deploying automated tools to handle routine inquiries. The system may also analyze historical data to predict the duration of the L2 state increase and adjust resource allocation accordingly. The goal is to ensure that service levels remain stable despite workforce fluctuations, particularly during transitions where higher-tier support demands may rise. The method may integrate with scheduling systems to optimize agent availability and prevent bottlenecks in the support pipeline.
6. The method of claim 1 , wherein the decrease to the agent workforce is initiated, and wherein an increase in a ratio of L2 to L1 states over a period of time is expected.
This invention relates to workforce management in a system where agents handle tasks or requests, such as customer service interactions. The problem addressed is optimizing agent allocation to improve efficiency, particularly when reducing the workforce while maintaining service quality. The method involves monitoring the states of agents, categorized as L1 (active/available) and L2 (busy/occupied), and dynamically adjusting the workforce based on changes in the ratio of L2 to L1 states over time. When a workforce reduction is initiated, the system expects an increase in the L2-to-L1 ratio, indicating higher demand or workload per agent. The method ensures that the reduction is implemented in a controlled manner, preventing overloading of remaining agents. This approach helps balance workload distribution, reduce idle time, and maintain operational efficiency during workforce adjustments. The system may use historical data, real-time metrics, or predictive analytics to determine the optimal timing and extent of workforce changes. The invention is applicable in call centers, help desks, or any environment where agent states are tracked and workload must be managed dynamically.
7. A system for workforce management in a contact center system comprising: at least one computer processor communicatively coupled to and configured to perform workforce management operations in the contact center system, wherein the at least one computer processor is further configured to: determine historical contact-agent interaction data of the contact center system; determine a preferred amount of choice for a choice-based pairing strategy for selecting waiting contacts out of queue order based on the historical contact-agent interaction data; determine an adjustment to an agent workforce of the contact center system to target the preferred amount of choice, wherein the adjustment comprises a decrease to the agent workforce; apply the choice-based pairing strategy to select waiting contacts out of queue order, wherein the targeting of the preferred amount of choice improves performance of the contact center system; and establish, in a switch of the contact center system, a communications channel between a waiting contact and an available agent based upon the applying of the choice-based pairing strategy to realize a performance gain for the contact center system attributable to the choice-based pairing strategy and the targeting of the preferred amount of choice.
The system optimizes workforce management in contact centers by dynamically adjusting agent staffing and contact routing to improve operational efficiency. Contact centers face challenges in balancing agent workload, customer wait times, and service quality. Traditional systems route contacts strictly by queue order, which may not always align with optimal agent-customer matching or workforce utilization. This system analyzes historical contact-agent interaction data to determine the ideal "amount of choice" for pairing waiting contacts with available agents. The "amount of choice" refers to the flexibility in selecting contacts out of queue order to better match agent skills, customer needs, or other performance factors. Based on this analysis, the system calculates an optimal reduction in the agent workforce while maintaining or improving contact center performance. By reducing agent staffing, the system increases the pool of waiting contacts available for strategic pairing, enhancing efficiency. The system applies a choice-based pairing strategy to select contacts out of queue order, targeting the preferred amount of choice. This approach improves overall contact center performance by optimizing agent utilization and reducing wait times. The system then establishes communication channels between selected contacts and available agents, ensuring performance gains are realized through the strategic pairing and workforce adjustments. The solution dynamically balances workforce size and contact routing to maximize efficiency without compromising service quality.
8. The system of claim 7 , wherein the at least one computer processor is further configured to: determine historical workforce size data or historical workforce capacity data.
This invention relates to workforce management systems that analyze historical workforce data to optimize staffing decisions. The system addresses the challenge of efficiently allocating workforce resources by leveraging past workforce size and capacity data to predict future needs and improve operational efficiency. The system includes at least one computer processor configured to collect and process historical workforce size data, which tracks the number of employees or staff members available over time, and historical workforce capacity data, which measures the productivity or output capacity of the workforce during specific periods. By analyzing these historical trends, the system can identify patterns, forecast future workforce requirements, and recommend adjustments to staffing levels or resource allocation. This helps organizations avoid understaffing or overstaffing, reduce labor costs, and enhance overall productivity. The system may integrate with existing workforce management tools or databases to gather and process the necessary data, ensuring accurate and up-to-date insights for decision-making. The invention is particularly useful in industries with fluctuating demand, such as healthcare, retail, or manufacturing, where workforce optimization directly impacts service quality and operational efficiency.
9. The system of claim 7 , wherein the adjustment further comprises an increase to the agent workforce.
A system for optimizing workforce allocation in a service environment, particularly in contact centers or customer support operations, addresses the challenge of dynamically adjusting staffing levels to meet fluctuating demand while maintaining service quality. The system monitors real-time performance metrics such as call volume, wait times, and agent availability to identify inefficiencies or bottlenecks. It then generates automated adjustments to workforce distribution, including reassigning agents to high-priority tasks or redistributing workloads across teams. In cases where existing staffing is insufficient, the system further includes the capability to increase the agent workforce by either activating reserve or on-call agents or by initiating hiring processes for additional personnel. The adjustments are based on predictive analytics and historical data to ensure optimal resource utilization and cost efficiency. The system integrates with existing workforce management tools and communication platforms to execute changes seamlessly without disrupting ongoing operations. This approach improves response times, reduces customer wait times, and enhances overall operational efficiency by proactively scaling the workforce in response to demand fluctuations.
10. The system of claim 7 , wherein the decrease to the agent workforce is initiated, and wherein an increase in contact queue size is expected based on the decrease to the agent workforce.
This invention relates to workforce management in contact centers, specifically systems that dynamically adjust agent staffing levels while anticipating changes in contact queue sizes. The system monitors real-time contact center metrics, including queue sizes and agent availability, to optimize staffing efficiency. When a reduction in the agent workforce is initiated, the system predicts an increase in the contact queue size due to the reduced workforce capacity. The system then implements automated adjustments to handle the anticipated surge in contacts, which may include redistributing workloads, rerouting contacts, or deploying additional resources. The invention ensures service quality is maintained despite workforce fluctuations by proactively managing contact volumes. The system integrates predictive analytics to forecast queue growth based on historical data, current trends, and the impact of workforce changes. This allows for preemptive adjustments rather than reactive measures, improving operational efficiency and customer satisfaction. The invention is particularly useful in environments with variable contact volumes, such as call centers, help desks, or customer service hubs, where sudden changes in staffing can lead to service disruptions.
11. The system of claim 7 , wherein the decrease to the agent workforce is initiated, and wherein an increase in a duration of an L2 state is expected.
A system for managing agent workforce allocation in a contact center environment addresses the problem of inefficient resource utilization during periods of reduced call volume. The system dynamically adjusts the number of agents available to handle customer interactions based on real-time demand. When a decrease in the agent workforce is initiated, the system also anticipates an increase in the duration of the L2 state, which refers to a low-activity period where fewer calls are received. This adjustment ensures that the remaining agents are optimally allocated to handle incoming interactions without overstaffing during low-demand periods. The system may include predictive analytics to forecast call volume trends and automate workforce adjustments accordingly. Additionally, the system may integrate with scheduling tools to modify agent shifts or assign tasks to agents during low-activity periods to maintain productivity. The solution aims to balance cost efficiency with service quality by aligning agent availability with expected call volumes, particularly during transitions between high and low activity states.
12. The system of claim 7 , wherein the decrease to the agent workforce is initiated, and wherein an increase in a ratio of L2 to L1 states over a period of time is expected.
This invention relates to workforce management systems for customer service operations, specifically addressing the challenge of optimizing agent staffing levels while maintaining service quality. The system dynamically adjusts the agent workforce based on real-time performance metrics and expected changes in service demand. A key feature is the ability to reduce the agent workforce when certain conditions are met, such as an anticipated increase in the ratio of L2 (higher complexity) to L1 (lower complexity) service states over a defined period. The system monitors these states to predict shifts in workload complexity and automatically triggers workforce adjustments to balance efficiency and service quality. The reduction in agents is initiated only when the system detects that the ratio of L2 to L1 states is rising, indicating a need for fewer agents handling simpler tasks while ensuring that more complex issues are adequately addressed. This approach helps organizations optimize labor costs without compromising customer satisfaction. The system integrates with existing customer service platforms to collect and analyze interaction data, enabling proactive workforce management. By anticipating changes in service demand patterns, the system ensures that staffing levels align with operational needs, reducing unnecessary overhead while maintaining responsiveness to customer inquiries.
13. An article of manufacture for workforce management in a contact center system comprising: a non-transitory processor readable medium; and instructions stored on the medium; wherein the instructions are configured to be readable from the medium by at least one computer processor communicatively coupled to and configured to perform workforce management operations in the contact center system and thereby cause the at least one computer processor to operate so as to: determine historical contact-agent interaction data of the contact center system; determine a preferred amount of choice for a choice-based pairing strategy for selecting waiting contacts out of queue order based on the historical contact-agent interaction data; determine an adjustment to an agent workforce of the contact center system to target the preferred amount of choice, wherein the adjustment comprises a decrease to the agent workforce; apply the choice-based pairing strategy to select waiting contacts out of queue order, wherein the targeting of the preferred amount of choice improves performance of the contact center system; and establish, in a switch of the contact center system, a communications channel between a waiting contact and an available agent based upon the applying of the choice-based pairing strategy to realize a performance gain for the contact center system attributable to the choice-based pairing strategy and the targeting of the preferred amount of choice.
This invention relates to workforce management in contact center systems, specifically optimizing agent workforce allocation to improve performance. The system addresses inefficiencies in traditional queue-based contact routing by dynamically adjusting agent staffing levels and implementing a choice-based pairing strategy. The system analyzes historical contact-agent interaction data to determine an optimal amount of choice for pairing waiting contacts with agents, prioritizing contacts out of strict queue order to enhance performance. By reducing the agent workforce to target this preferred choice level, the system ensures that agents have meaningful options when selecting contacts, leading to better matching and improved contact center efficiency. The system then applies this strategy to route contacts, establishing communication channels between agents and contacts based on the optimized pairing. This approach improves overall contact center performance by balancing workforce size with strategic contact routing, reducing wait times and increasing agent productivity. The invention is implemented via a non-transitory processor-readable medium containing instructions executable by a computer processor to perform these operations.
14. The article of manufacture of claim 13 , wherein the at least one computer processor is further caused to operate so as to: determine historical workforce size data or historical workforce capacity data.
This invention relates to workforce management systems that analyze historical workforce data to optimize staffing decisions. The system collects and processes historical workforce size data or historical workforce capacity data to identify trends, predict future workforce needs, and improve resource allocation. The system includes at least one computer processor configured to analyze this data, enabling organizations to make data-driven decisions about hiring, scheduling, and workforce planning. By leveraging historical data, the system helps businesses maintain optimal workforce levels, reduce costs, and enhance operational efficiency. The invention addresses the challenge of accurately forecasting workforce requirements by utilizing past performance metrics to inform current and future staffing strategies. The system may integrate with existing human resources or enterprise resource planning (ERP) systems to provide real-time insights and recommendations. This approach ensures that organizations can adapt to changing demands while minimizing overstaffing or understaffing issues. The invention is particularly useful in industries with fluctuating workloads, such as healthcare, retail, and manufacturing, where precise workforce planning is critical for maintaining productivity and service quality.
15. The article of manufacture of claim 13 , wherein the adjustment further comprises an increase to the agent workforce.
This invention relates to an article of manufacture designed to optimize workforce management in a system where agents handle tasks or requests. The problem addressed is the need to dynamically adjust workforce levels to efficiently manage workload fluctuations, ensuring timely task completion while minimizing resource waste. The article of manufacture includes a processor and memory storing instructions that, when executed, enable the system to monitor workload metrics such as task volume, agent availability, and task completion rates. Based on these metrics, the system calculates an optimal workforce adjustment to maintain efficiency. The adjustment may involve increasing or decreasing the number of agents assigned to tasks, redistributing tasks among agents, or modifying agent schedules. In one embodiment, the adjustment includes increasing the agent workforce to handle a surge in task volume, ensuring that tasks are processed within acceptable timeframes. The system may also incorporate predictive analytics to anticipate workload changes and proactively adjust the workforce. The invention aims to improve operational efficiency, reduce costs, and enhance service quality by dynamically aligning workforce capacity with demand.
16. The article of manufacture of claim 13 , wherein the decrease to the agent workforce is initiated, and wherein an increase in contact queue size is expected based on the decrease to the agent workforce.
This invention relates to workforce management systems for contact centers, specifically addressing the challenge of dynamically adjusting agent staffing levels while anticipating changes in contact queue sizes. The system monitors real-time contact center metrics, including call volumes, agent availability, and service level agreements (SLAs), to predict the impact of workforce adjustments. When a reduction in the agent workforce is initiated, the system automatically forecasts an increase in the contact queue size due to the reduced staffing. This predictive capability allows the system to proactively adjust routing strategies, escalation rules, or other operational parameters to maintain service quality despite the workforce change. The system may also integrate historical data and machine learning models to refine its predictions over time. By anticipating queue size fluctuations, the invention helps prevent service degradation and ensures efficient resource allocation in contact centers. The solution is particularly useful for organizations needing to balance cost efficiency with customer satisfaction during staffing transitions.
17. The article of manufacture of claim 13 , wherein the decrease to the agent workforce is initiated, and wherein an increase in a duration of an L2 state is expected.
This invention relates to power management in computing systems, specifically addressing the challenge of optimizing energy efficiency during periods of reduced workload. The system dynamically adjusts the agent workforce—a group of processing units or cores—in response to workload demands. When a decrease in the agent workforce is initiated, the system also anticipates an increase in the duration of the L2 state, a low-power idle state for the processing units. This adjustment helps balance performance and energy consumption by reducing active processing resources while extending idle periods to conserve power. The system may include mechanisms to monitor workload levels, predict future demand, and coordinate transitions between active and idle states to minimize energy waste without compromising responsiveness. The invention is particularly useful in data centers, servers, or other high-performance computing environments where energy efficiency is critical. By intelligently managing the agent workforce and L2 state duration, the system ensures optimal power usage during low-activity periods while maintaining the ability to quickly scale up when demand increases.
Unknown
December 8, 2020
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